The PV SoilSayer utilizes industry standard PV performance modeling software ( PVLib), as well as advanced machine learning algorithms combined with local TMY weather datasets and PV system soiling estimations to calculate estimated soiling losses, optimal cleaning schedules and associated costs.
The PV SoilSayer is designed to give users full control over modeling the cost trade offs associated with soiling. Users can choose from a number of different options for calculating soiling losses or uploading measured data.
Additionally, users can choose from a number of different modeling configuration options depending on the required outputs.
Users can choose to run different configurations of cleaning cycles, cleaning costs, soiling losses and rainfall events in order to negotiate cleaning costs and align cleaning cycles with O&M budgets.
Detailed reports provide users with detailed estimates of cost trade offs associated with a specified clean cycle.
Users can compare the resulting soiling losses, associated costs, and value of energy gain due to resulting clean cycles in order to make informed decisions about cleaning costs, and the frequency and timing for cleaning the PV system.
Interactive charts provide users with valuable insights into daily, monthly, and annual precipitation patterns, soiling trends and associated losses.